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'''DiaBLA: Dialogue Bilingue datset''' |
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import json |
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import datasets |
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from datasets.features import ClassLabel |
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logger = datasets.logging.get_logger(__name__) |
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_CITATION = '''\ |
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@article{bawden_DiaBLa:-A-Corpus-of_2021, |
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author = {Bawden, Rachel and Bilinski, Eric and Lavergne, Thomas and Rosset, Sophie}, |
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doi = {10.1007/s10579-020-09514-4}, |
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title = {DiaBLa: A Corpus of Bilingual Spontaneous Written Dialogues for Machine Translation}, |
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year = {2021}, |
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journal = {Language Resources and Evaluation}, |
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publisher = {Springer Verlag}, |
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volume = {55}, |
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pages = {635--660}, |
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url = {https://hal.inria.fr/hal-03021633}, |
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pdf = {https://hal.inria.fr/hal-03021633/file/diabla-lre-personal-formatting.pdf}, |
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} |
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''' |
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_DESCRIPTION = '''\ |
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English-French parallel dataset for the evaluation of \ |
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Machine Translation (MT) for informal, written bilingual dialogue. |
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''' |
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_URLS = { |
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'test': 'DiaBLa.json', |
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} |
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class DiablaConfig(datasets.BuilderConfig): |
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'''BuilderConfig for DiaBLa.''' |
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def __init__(self, **kwargs): |
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"""BuilderConfig for DiaBLa. |
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Args: |
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**kwargs: keyword arguments forwarded to super. |
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""" |
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super(DiablaConfig, self).__init__(**kwargs) |
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class Diabla(datasets.GeneratorBasedBuilder): |
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'''DiaBLa: English-French parallel dataset of bilingual dialogue''' |
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BUILDER_CONFIGS = [ |
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DiablaConfig( |
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name='plain_text', |
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version=datasets.Version('1.0.0', ''), |
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description='Plain text', |
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), |
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] |
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def _info(self): |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=datasets.Features( |
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{ |
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'id': datasets.Value('string'), |
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'orig': datasets.Value('string'), |
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'norm': datasets.Value('string'), |
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'mt': datasets.Value('string'), |
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'ref': datasets.Value('string'), |
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'utterance_meta': datasets.features.Sequence( |
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{ |
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'eval-judgment': ClassLabel(num_classes=3, names=['poor', 'medium', 'perfect']), |
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'eval-verbatim': datasets.Value('string'), |
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'eval-problems': datasets.features.Sequence( |
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[ |
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ClassLabel(num_classes=5, names=['coherence', 'grammar', 'meaning', 'word choice', 'style']) |
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] |
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), |
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'lang': ClassLabel(num_classes=2, names=['en', 'fr']), |
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} |
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), |
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'dialogue_meta': datasets.features.Sequence( |
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{ |
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'start_time': datasets.Value('string'), |
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'end_time' : datasets.Value('string'), |
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'translation_model': datasets.Value('string'), |
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'final_evaluation_user1': datasets.features.Sequence( |
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{ |
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'style': ClassLabel(num_classes=4, names=['poor', 'average', 'good', 'excellent']), |
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'coherence': ClassLabel(num_classes=4, names=['poor', 'average', 'good', 'excellent']), |
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'grammaticality': ClassLabel(num_classes=4, names=['poor', 'average', 'good', 'excellent']), |
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'meaning': ClassLabel(num_classes=4, names=['poor', 'average', 'good', 'excellent']), |
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'word_choice': ClassLabel(num_classes=4, names=['poor', 'average', 'good', 'excellent']) |
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} |
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), |
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'final_evaluation_user2': datasets.features.Sequence( |
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{ |
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'style': ClassLabel(num_classes=4, names=['poor', 'average', 'good', 'excellent']), |
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'coherence': ClassLabel(num_classes=4, names=['poor', 'average', 'good', 'excellent']), |
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'grammaticality': ClassLabel(num_classes=4, names=['poor', 'average', 'good', 'excellent']), |
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'meaning': ClassLabel(num_classes=4, names=['poor', 'average', 'good', 'excellent']), |
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'word_choice': ClassLabel(num_classes=4, names=['poor', 'average', 'good', 'excellent']) |
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} |
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), |
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'scenario': datasets.features.Sequence( |
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[ |
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[ |
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datasets.Value("string") |
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] |
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] |
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), |
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'user1': datasets.features.Sequence( |
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{ |
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'rolenum': datasets.Value('int64'), |
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'role': datasets.features.Sequence( |
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[ |
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datasets.Value('string') |
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] |
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) |
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'initiated_dialogue': datasets.Value('bool'), |
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'turn_number': datasets.Value('int64'), |
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'lang': datasets.Value('string'), |
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} |
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), |
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'user2': datasets.features.Sequence( |
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{ |
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'rolenum': datasets.Value('int64'), |
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'role': datasets.features.Sequence( |
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[ |
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datasets.Value('string') |
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] |
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) |
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'initiated_dialogue': datasets.Value('bool'), |
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'turn_number': datasets.Value('int64'), |
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'lang': datasets.Value('string'), |
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} |
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) |
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} |
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), |
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'dialogue_history': datasets.features.Sequence( |
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[ |
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datasets.features.Sequence( |
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{ |
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'id': datasets.Value('string'), |
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'orig': datasets.Value('string'), |
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'norm': datasets.Value('string'), |
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'mt': datasets.Value('string'), |
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'ref': datasets.Value('string'), |
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'utterance_meta': datasets.features.Sequence( |
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{ |
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'judgment': ClassLabel(num_classes=3, names=['poor', 'medium', 'perfect']), |
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'verbatim': datasets.Value("string"), |
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'problems': datasets.features.Sequence( |
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[ |
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ClassLabel(num_classes=5, |
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names=['coherence', 'grammar', 'meaning', 'word choice', 'style']) |
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] |
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), |
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'lang': ClassLabel(num_classes=2, names=['en', 'fr']), |
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} |
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), |
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}), |
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] |
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) |
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} |
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), |
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supervised_keys=None, |
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homepage='https://github.com/rbawden/DiaBLa-dataset', |
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citation=_CITATION, |
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task_templates=[ |
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], |
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) |
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def _split_generators(self, dl_manager): |
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downloaded_files = dl_manager.download_and_extract(_URLS) |
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return [datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={'filepath': downloaded_files['test']})] |
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def _generate_examples(self, filepath): |
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'''This function returns the examples in the raw (text) form.''' |
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logger.info("generating examples from = %s", filepath) |
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key = 0 |
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with open(filepath, encoding="utf-8") as f: |
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diabla = json.load(f) |
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for dialogue_name in sorted(diabla['dialogues']): |
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dialogue_history = [] |
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dialogue = diabla['dialogues'][dialogue_name] |
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dialogue_info_keys = ['start_time', 'end_time', 'scenario', |
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'user1', 'user2', 'translation_model', |
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'final_evaluation_user1', 'final_evaluation_user2'] |
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for info_to_remove in ['eval-stage', 'useragent']: |
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for user in 'user1', 'user2': |
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if info_to_remove in dialogue[user]: |
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del dialogue[user][info_to_remove] |
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dialogue_info = {k: dialogue[k] for k in dialogue_info_keys} |
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if dialogue_info['end_time'] is None: |
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dialogue_info['end_time'] = '' |
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for info_to_remove in ['interface','verbatim_quality', |
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'particular_problems', 'tech', |
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'would_use', 'timestamp', 'technical_issue']: |
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for final_eval in 'final_evaluation_user1', 'final_evaluation_user2': |
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if info_to_remove in dialogue_info[final_eval]: |
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del dialogue_info[final_eval][info_to_remove] |
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for utterance_id in dialogue['utterances']: |
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utterance = dialogue['utterances'][utterance_id] |
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utterance_info_keys = ['judgment', 'verbatim', 'problems'] |
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utterance_info = {'eval-' + k: utterance['eval'][k] for k in utterance_info_keys} |
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if utterance_info['eval-judgment'] is None: |
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utterance_info['eval-judgment'] = '' |
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utterance_info['lang'] = utterance['language'] |
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original_text = utterance['original_text'] |
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mt_text = utterance['postprocessed_text'] |
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reference_text = utterance['reference_translation'] |
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normalised_text = utterance['normalised_version'] |
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id_ = dialogue_name + '_' + utterance_id |
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utterance_instance = { |
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'orig': original_text, |
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'norm': normalised_text, |
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'mt': mt_text, |
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'id': id_, |
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'ref': reference_text, |
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'utterance_meta': utterance_info |
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} |
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dialogue_history.append(utterance_instance.copy()) |
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utterance_instance['dialogue_meta'] = dialogue_info |
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utterance_instance['dialogue_history'] = dialogue_history |
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yield id_, utterance_instance |
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